The graph represents a network of 80,831 Twitter users whose recent tweets contained "long covid", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 100,000 tweets, tweeted between 11/1/2022 7:30:50 AM and 11/19/2022 7:30:50 AM. The network was obtained from Twitter on Saturday, 19 November 2022 at 16:45 UTC.
The tweets in the network were tweeted over the 4415-day, 4-hour, 23-minute period from Monday, 18 October 2010 at 11:07 UTC to Saturday, 19 November 2022 at 15:30 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
The graph is directed.
The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Author Description
Vertices : 80831
Unique Edges : 50888
Edges With Duplicates : 221572
Total Edges : 272460
Number of Edge Types : 9
Retweet : 70493
MentionsInRetweet : 88438
Replies to : 28968
MentionsInReplyTo : 63981
Mentions : 4568
MentionsInQuoteReply : 1388
Quote : 2443
Tweet : 11188
MentionsInQuote : 993
Self-Loops : 17216
Reciprocated Vertex Pair Ratio : 0.0390324270562064
Reciprocated Edge Ratio : 0.0751322596673779
Connected Components : 7393
Single-Vertex Connected Components : 3497
Maximum Vertices in a Connected Component : 64528
Maximum Edges in a Connected Component : 244525
Maximum Geodesic Distance (Diameter) : 18
Average Geodesic Distance : 5.190038
Graph Density : 2.09459768021792E-05
Modularity : 0.432121
NodeXL Version : 1.0.1.507
Data Import : The graph represents a network of 80,831 Twitter users whose recent tweets contained "long covid", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 100,000 tweets, tweeted between 11/1/2022 7:30:50 AM and 11/19/2022 7:30:50 AM. The network was obtained from Twitter on Saturday, 19 November 2022 at 16:45 UTC.
The tweets in the network were tweeted over the 4415-day, 4-hour, 23-minute period from Monday, 18 October 2010 at 11:07 UTC to Saturday, 19 November 2022 at 15:30 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch2
Graph Term : long covid
Groups : The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
Edge Color : Edge Weight
Edge Width : Edge Weight
Edge Alpha : Edge Weight
Vertex Radius : Betweenness Centrality
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[44200] long,covid [12065] long,term [4789] covid,long [4269] pfizer,moderna [4211] covid,vaccines [3671] term,health [3649] health,issues [3443] moderna,pfizer [3344] covid,19 [3286] cause,long Top Word Pairs in Tweet in G1:
[17995] long,covid [2451] long,term [1131] covid,19 [834] covid,long [822] rowlsmanthorpe,long [629] immunology,long [509] term,effects [485] re,learning [473] term,damage [470] risk,long Top Word Pairs in Tweet in G2:
[3763] long,covid [2050] covid,long [1916] something,covid [1912] died,something [1912] accident,suicide [1912] overdose,accident [1912] covid,overdose [1911] 400,died [1908] put,400 [1908] simply,put Top Word Pairs in Tweet in G3:
[2677] long,terme [2554] pfizer,moderna [2551] problèmes,cardiaques [2551] essais,cliniques [2550] moderna,lancent [2549] visant,surveiller [2549] surveiller,éventuels [2549] cliniques,visant [2549] lancent,essais [2549] éventuels,problèmes Top Word Pairs in Tweet in G4:
[3908] long,covid [784] angebliche,corona [784] opfer,angebliche [565] marcoschlaepfer,komisch [398] corona,tote [397] covid,kongress [393] corona,opfer [393] covid,opfer [392] angebliche,long [392] früher,angebliche Top Word Pairs in Tweet in G5:
[2952] long,term [2790] cause,long [2785] covid,vaccines [2779] vaccines,cause [2776] term,health [2776] health,issues [2769] moderna,pfizer [2765] whether,covid [2760] investigate,whether [2757] pfizer,investigate Top Word Pairs in Tweet in G6:
[1836] long,covid [525] long,term [415] covid,19 [159] covid,long [106] long,time [100] term,effects [92] pfizer,moderna [91] covid,symptoms [78] brain,fog [72] covid,changed Top Word Pairs in Tweet in G7:
[2454] 日本の半分人口で18万人の死亡,日本4万7千人 [2454] は正しいです,結果なにが起きたかと言うと [2454] 全人口の2,がコロナ後遺症 [2454] 英国ではワクチン関係なく殆どの人が一度は感染,は正しいです [2454] 宮沢氏に再びブロックされましたが,英国の件でお伝えします [2454] 日本4万7千人,全人口の2 [2454] 英国の件でお伝えします,英国ではワクチン関係なく殆どの人が一度は感染 [2454] 結果なにが起きたかと言うと,日本の半分人口で18万人の死亡 [2453] mikito_777,宮沢氏に再びブロックされましたが [175] long,covid Top Word Pairs in Tweet in G8:
[1074] long,covid [1032] long,term [685] term,disability [680] tell,reason [680] reason,long [680] kid,eye [680] having,look [680] imagine,having [680] look,kid [680] years,tell Top Word Pairs in Tweet in G9:
[3081] long,covid [1832] rowlsmanthorpe,long [1502] very,bad [1502] knew,already [1502] hopeful,story [1502] covid,wrecked [1502] bad,knew [1502] wrecked,life [1502] life,bad [1502] tell,hopeful Top Word Pairs in Tweet in G10:
[1284] long,term [1065] half,million [1064] workforce,long [1063] term,illness [1060] people,dropped [1060] million,additional [1060] dropped,workforce [1060] additional,people [1059] illness,2019 [1059] 2019,huge Top Replied-To in Entire Graph:
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Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Mentioned in G9:
Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
Top Tweeters in G2:
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Top Tweeters in G10: